This study aims to examine volatility spillover among equity and commodity markets of the United States. The analysis focuses on crude oil (Brent and WTI [West Texas Intermediate]), rice, and gasoline. For the analysis, generalized autoregressive conditional heteroscedasticity (GARCH) (1, 1) model is applied on monthly data for the period of February 2005 to December 2016. Results show that there is no volatility spillover from commodity market (gold, oil, gas, and rice) to equity market, whereas it only exists in few commodity markets, from oil to rice and gas. The study also finds that there is neither mean spillover nor volatility spillover among gold and equity market; therefore, investor can invest in equity and gold to diversify risk of portfolio.

No abstract

This study aims to find the impact of change in economic policy uncertainty (EPU) on the returns and volatilities of 11 CRSP Ziman value-weighted US real estate investment trusts (REITs) during 1985–2016. The results indicate that the change in EPU has a positive relationship with volatility and a negative one with the REITs returns. Among EPU components, news-based component has the major impact than the others. Change in economic policy uncertainty has a significant impact on the returns of all the indices except hybrid, healthcare and unclassified REITs after controlling for macroeconomic variables. Whereas, the volatility is mainly explained by its own past values and macroeconomic variables.

This study aims to identify the number of stocks required to form a well-diversified portfolio in Pakistan. To identify the optimal number of securities we collected data from Bloomberg for the period of 2009-2015. Companies are randomly selected from the Pakistan Stock Exchange. The 40 portfolios are formed by adding different securities (without replacement) by assigning equal weights to all the securities. The study concludes that a portfolio of 20 securities can diversify significant amount of risk

This study investigates whether Google Search Volume Indices (GSVIs) bring shifts in the expected return of prominent pricing factors in comparison to the Volatility Index (VIX). The results show that compared to VIX, GSVIs bring less significant changes in expected premium on Fama-French's five-factors and q-factors. Pessimistic sentiment indices (Market Crash and Bear Market) predict more significant variation in the premium of prominent pricing factors than optimistic sentiment indices (Market Rally and Bull Market), and also have a significant correlation with VIX representing downside risk. Furthermore, the sentiment indices are better in predicting premium on five-factors than q-factors.